Empirical Modeling of Transient Emissions and Transient Response for Transient Optimization

Empirical models for engine-out oxides of Nitrogen (NO_x) and smoke emissions have been developed for the purpose of minimizing transient emissions while maintaining transient response. Three major issues have been addressed: data acquisition, data processing and modeling method. Real and virtual transient parameters have been identified for acquisition. Accounting for the phase shift between transient engine events and transient emission measurements has been shown to be very important to the quality of model predictions. Several methods have been employed to account for the transient transport delays and sensor lags which constitute the phase shift. Finally several different empirical modeling methods have been used to determine the most suitable modeling method for transient emissions. These modeling methods include several kinds of neural networks, global regression and localized regression. Global regression models have been shown to be the most suitable technique for transient emission predictions. Within global regression models, the best model form, model transforms and model terms have been explored. The chosen models, built on transient data, processed to account for transient lags and sensor delays, have been shown to predict transient emissions and transient response over different transient cycles with reasonable accuracy.